Prevalence and Clinico‐Pathologic Profiles of Nonodontogenic Cysts of the Oral and Maxillofacial Region: A Multicentre Study
Bibliographic record
Abstract
Objectives: To determine the prevalences, demographic, and pathologic profiles of patients diagnosed with nonodontogenic cysts (NOCs) in the oral and maxillofacial regions. Materials and Methods: Biopsy records from the participating institutions from 2000 to 2024 were studied for lesions diagnosed in the NOC category. Demographic profiles, the locations, and pathologic diagnoses were collected. Data were analyzed by using IBM SPSS Statistics version 29.0. Results: A total of 183,132 cases were obtained and 1864 cases (1.02%) were diagnosed as NOCs. The age of the patient ranged from 1 to 96 years with mean ± SD = 49.03 ± 18.43 years. The overall male-to-female ratio was 1.08:1. The majority of the lesions were encountered in the soft tissue. The most prevalent NOC was nasopalatine duct cyst followed by mucus retention cysts and nasolabial cysts. Conclusions: This study is the largest study on NOCs from Southeast Asia, the Middle East, and North America. The frequency of NOCs found in this studied population is somewhat different from those reported in previous studies. This study offers a valuable database for clinicians to facilitate the clinical differential diagnoses along with for the pathologists in rendering the final diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".